Overview

Dataset statistics

Number of variables14
Number of observations203693
Missing cells0
Missing cells (%)0.0%
Duplicate rows5
Duplicate rows (%)< 0.1%
Total size in memory23.3 MiB
Average record size in memory120.0 B

Variable types

Numeric13
Categorical1

Alerts

Dataset has 5 (< 0.1%) duplicate rowsDuplicates
CO has 9316 (4.6%) zerosZeros
Benzene has 28449 (14.0%) zerosZeros
Toluene has 10273 (5.0%) zerosZeros
Xylene has 53724 (26.4%) zerosZeros

Reproduction

Analysis started2024-06-25 13:42:10.128538
Analysis finished2024-06-25 13:42:32.081012
Duration21.95 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

PM2.5
Real number (ℝ)

Distinct14705
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.759292
Minimum0.01
Maximum999.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2024-06-25T19:12:32.200254image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile9.5
Q124
median42
Q369
95-th percentile127.75
Maximum999.99
Range999.98
Interquartile range (IQR)45

Descriptive statistics

Standard deviation47.386817
Coefficient of variation (CV)0.89817008
Kurtosis31.281296
Mean52.759292
Median Absolute Deviation (MAD)21
Skewness3.9580326
Sum10746698
Variance2245.5105
MonotonicityNot monotonic
2024-06-25T19:12:32.355930image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 1783
 
0.9%
17 834
 
0.4%
27 815
 
0.4%
32 794
 
0.4%
30 791
 
0.4%
33 767
 
0.4%
21 766
 
0.4%
26 750
 
0.4%
24 724
 
0.4%
28 721
 
0.4%
Other values (14695) 194948
95.7%
ValueCountFrequency (%)
0.01 1
 
< 0.1%
0.03 2
 
< 0.1%
0.04 1
 
< 0.1%
0.07 3
 
< 0.1%
0.1 1
 
< 0.1%
0.14 1
 
< 0.1%
0.15 1
 
< 0.1%
0.16 2
 
< 0.1%
0.2 3
 
< 0.1%
0.25 33
< 0.1%
ValueCountFrequency (%)
999.99 1
 
< 0.1%
971.88 1
 
< 0.1%
887 12
< 0.1%
830.4 1
 
< 0.1%
830 1
 
< 0.1%
796.92 1
 
< 0.1%
788 12
< 0.1%
745.75 1
 
< 0.1%
740 2
 
< 0.1%
710.75 1
 
< 0.1%

PM10
Real number (ℝ)

Distinct21982
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.39596
Minimum1
Maximum999.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2024-06-25T19:12:32.513187image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25.25
Q158
median94
Q3140.75
95-th percentile242.5
Maximum999.99
Range998.99
Interquartile range (IQR)82.75

Descriptive statistics

Standard deviation76.70476
Coefficient of variation (CV)0.70116629
Kurtosis12.773767
Mean109.39596
Median Absolute Deviation (MAD)40
Skewness2.4910643
Sum22283192
Variance5883.6203
MonotonicityNot monotonic
2024-06-25T19:12:32.653811image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94 3137
 
1.5%
86 400
 
0.2%
69 365
 
0.2%
70 360
 
0.2%
80 358
 
0.2%
84 358
 
0.2%
66 356
 
0.2%
88 355
 
0.2%
71 354
 
0.2%
72 354
 
0.2%
Other values (21972) 197296
96.9%
ValueCountFrequency (%)
1 4
 
< 0.1%
1.5 2
 
< 0.1%
2 11
< 0.1%
2.25 5
< 0.1%
2.5 1
 
< 0.1%
2.75 3
 
< 0.1%
3 4
 
< 0.1%
3.25 2
 
< 0.1%
3.5 3
 
< 0.1%
3.58 1
 
< 0.1%
ValueCountFrequency (%)
999.99 35
< 0.1%
988.65 1
 
< 0.1%
978 1
 
< 0.1%
977 1
 
< 0.1%
962.7 1
 
< 0.1%
928.88 1
 
< 0.1%
927.9 1
 
< 0.1%
924 1
 
< 0.1%
923.49 1
 
< 0.1%
920 1
 
< 0.1%

NO
Real number (ℝ)

Distinct10899
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.901996
Minimum0.01
Maximum497.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2024-06-25T19:12:32.810062image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.49
Q11.6
median3.85
Q310.04
95-th percentile47.5
Maximum497.3
Range497.29
Interquartile range (IQR)8.44

Descriptive statistics

Standard deviation28.00937
Coefficient of variation (CV)2.3533338
Kurtosis56.194735
Mean11.901996
Median Absolute Deviation (MAD)2.75
Skewness6.4120859
Sum2424353.3
Variance784.52483
MonotonicityNot monotonic
2024-06-25T19:12:32.950683image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4 1173
 
0.6%
1.1 1022
 
0.5%
1.15 1004
 
0.5%
1.25 980
 
0.5%
1.2 966
 
0.5%
0.8 964
 
0.5%
1 964
 
0.5%
1.3 942
 
0.5%
1.05 936
 
0.5%
0.9 923
 
0.5%
Other values (10889) 193819
95.2%
ValueCountFrequency (%)
0.01 29
 
< 0.1%
0.02 13
 
< 0.1%
0.03 18
 
< 0.1%
0.04 16
 
< 0.1%
0.05 20
 
< 0.1%
0.06 8
 
< 0.1%
0.07 14
 
< 0.1%
0.08 27
 
< 0.1%
0.09 8
 
< 0.1%
0.1 421
0.2%
ValueCountFrequency (%)
497.3 1
< 0.1%
488.45 1
< 0.1%
486.25 1
< 0.1%
472.6 1
< 0.1%
466.12 1
< 0.1%
465.15 1
< 0.1%
463.55 1
< 0.1%
459.45 1
< 0.1%
455.95 1
< 0.1%
448.65 1
< 0.1%

NO2
Real number (ℝ)

Distinct11624
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.645782
Minimum0.01
Maximum432.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2024-06-25T19:12:33.091278image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile4.62
Q113.28
median26.48
Q346.59
95-th percentile86.72
Maximum432.3
Range432.29
Interquartile range (IQR)33.31

Descriptive statistics

Standard deviation27.180908
Coefficient of variation (CV)0.80785484
Kurtosis5.1523161
Mean33.645782
Median Absolute Deviation (MAD)15.18
Skewness1.6775265
Sum6853410.4
Variance738.80177
MonotonicityNot monotonic
2024-06-25T19:12:33.231933image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 712
 
0.3%
91.66 310
 
0.2%
9 298
 
0.1%
9.02 179
 
0.1%
8.99 163
 
0.1%
8.98 160
 
0.1%
9.2 152
 
0.1%
9.01 151
 
0.1%
14.9 149
 
0.1%
10 145
 
0.1%
Other values (11614) 201274
98.8%
ValueCountFrequency (%)
0.01 9
 
< 0.1%
0.02 5
 
< 0.1%
0.03 6
 
< 0.1%
0.04 2
 
< 0.1%
0.07 1
 
< 0.1%
0.09 1
 
< 0.1%
0.1 55
< 0.1%
0.12 4
 
< 0.1%
0.13 1
 
< 0.1%
0.14 1
 
< 0.1%
ValueCountFrequency (%)
432.3 1
< 0.1%
395.32 1
< 0.1%
372.25 1
< 0.1%
352.72 1
< 0.1%
349.47 1
< 0.1%
342.25 1
< 0.1%
339.55 1
< 0.1%
326.1 1
< 0.1%
313.7 1
< 0.1%
309.32 1
< 0.1%

NOx
Real number (ℝ)

Distinct14517
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.361168
Minimum0
Maximum499.2
Zeros50
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2024-06-25T19:12:33.372528image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.616
Q111.1
median20.5
Q335.47
95-th percentile91.48
Maximum499.2
Range499.2
Interquartile range (IQR)24.37

Descriptive statistics

Standard deviation36.472885
Coefficient of variation (CV)1.2013005
Kurtosis31.66945
Mean30.361168
Median Absolute Deviation (MAD)10.88
Skewness4.5545015
Sum6184357.4
Variance1330.2714
MonotonicityNot monotonic
2024-06-25T19:12:33.519379image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
117.74 306
 
0.2%
9.25 190
 
0.1%
9.05 188
 
0.1%
9.2 187
 
0.1%
8.75 186
 
0.1%
10.8 185
 
0.1%
8.5 185
 
0.1%
8.45 182
 
0.1%
10.1 182
 
0.1%
9.35 182
 
0.1%
Other values (14507) 201720
99.0%
ValueCountFrequency (%)
0 50
< 0.1%
0.01 2
 
< 0.1%
0.03 6
 
< 0.1%
0.04 2
 
< 0.1%
0.05 10
 
< 0.1%
0.06 2
 
< 0.1%
0.07 3
 
< 0.1%
0.08 5
 
< 0.1%
0.09 2
 
< 0.1%
0.1 10
 
< 0.1%
ValueCountFrequency (%)
499.2 1
< 0.1%
498.13 1
< 0.1%
497.7 1
< 0.1%
496.75 1
< 0.1%
496.5 1
< 0.1%
496.43 1
< 0.1%
492.4 1
< 0.1%
489.7 1
< 0.1%
488.07 1
< 0.1%
487.75 1
< 0.1%

NH3
Real number (ℝ)

Distinct7974
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.330787
Minimum0.01
Maximum465.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2024-06-25T19:12:33.800630image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile3.75
Q19.08
median13.62
Q321.42
95-th percentile41.75
Maximum465.8
Range465.79
Interquartile range (IQR)12.34

Descriptive statistics

Standard deviation15.206833
Coefficient of variation (CV)0.87744617
Kurtosis88.523557
Mean17.330787
Median Absolute Deviation (MAD)5.74
Skewness5.9989461
Sum3530160
Variance231.24776
MonotonicityNot monotonic
2024-06-25T19:12:33.941253image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 411
 
0.2%
10.3 349
 
0.2%
13 334
 
0.2%
10.05 332
 
0.2%
10.2 319
 
0.2%
10.25 316
 
0.2%
12 315
 
0.2%
10.38 315
 
0.2%
10.45 311
 
0.2%
10.5 310
 
0.2%
Other values (7964) 200381
98.4%
ValueCountFrequency (%)
0.01 10
 
< 0.1%
0.02 1
 
< 0.1%
0.03 7
 
< 0.1%
0.04 6
 
< 0.1%
0.05 5
 
< 0.1%
0.06 2
 
< 0.1%
0.07 1
 
< 0.1%
0.08 7
 
< 0.1%
0.09 1
 
< 0.1%
0.1 207
0.1%
ValueCountFrequency (%)
465.8 1
< 0.1%
445.9 1
< 0.1%
439.88 1
< 0.1%
430.21 1
< 0.1%
418.5 1
< 0.1%
413.78 1
< 0.1%
407.8 1
< 0.1%
399.48 1
< 0.1%
397.21 1
< 0.1%
391.89 1
< 0.1%

CO
Real number (ℝ)

ZEROS 

Distinct667
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.70146146
Minimum0
Maximum48.52
Zeros9316
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2024-06-25T19:12:34.081884image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.02
Q10.35
median0.58
Q30.89
95-th percentile1.69
Maximum48.52
Range48.52
Interquartile range (IQR)0.54

Descriptive statistics

Standard deviation0.60633149
Coefficient of variation (CV)0.86438318
Kurtosis234.48784
Mean0.70146146
Median Absolute Deviation (MAD)0.26
Skewness6.2919793
Sum142882.79
Variance0.36763788
MonotonicityNot monotonic
2024-06-25T19:12:34.224592image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9316
 
4.6%
0.8 2767
 
1.4%
0.4 2700
 
1.3%
0.45 2558
 
1.3%
0.7 2442
 
1.2%
0.3 2435
 
1.2%
0.47 2426
 
1.2%
0.55 2423
 
1.2%
0.33 2422
 
1.2%
0.34 2387
 
1.2%
Other values (657) 171817
84.4%
ValueCountFrequency (%)
0 9316
4.6%
0.01 288
 
0.1%
0.02 920
 
0.5%
0.03 343
 
0.2%
0.04 267
 
0.1%
0.05 379
 
0.2%
0.06 303
 
0.1%
0.07 309
 
0.2%
0.08 535
 
0.3%
0.09 364
 
0.2%
ValueCountFrequency (%)
48.52 1
< 0.1%
20.95 1
< 0.1%
19.95 1
< 0.1%
17.47 1
< 0.1%
12.97 1
< 0.1%
11.64 1
< 0.1%
11.6 1
< 0.1%
11.43 1
< 0.1%
11.42 1
< 0.1%
11.35 1
< 0.1%

SO2
Real number (ℝ)

Distinct5885
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.7836472
Minimum0.01
Maximum199.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2024-06-25T19:12:34.399366image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile1.12
Q13.58
median6.7
Q311.77
95-th percentile28.17
Maximum199.9
Range199.89
Interquartile range (IQR)8.19

Descriptive statistics

Standard deviation11.230091
Coefficient of variation (CV)1.147843
Kurtosis35.587034
Mean9.7836472
Median Absolute Deviation (MAD)3.7
Skewness4.4821272
Sum1992860.4
Variance126.11495
MonotonicityNot monotonic
2024-06-25T19:12:34.549134image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 514
 
0.3%
3.2 467
 
0.2%
2.9 456
 
0.2%
3.65 456
 
0.2%
2.8 454
 
0.2%
2.5 453
 
0.2%
3 453
 
0.2%
4.2 451
 
0.2%
1.5 450
 
0.2%
4 448
 
0.2%
Other values (5875) 199091
97.7%
ValueCountFrequency (%)
0.01 8
 
< 0.1%
0.02 7
 
< 0.1%
0.03 23
 
< 0.1%
0.04 3
 
< 0.1%
0.05 47
 
< 0.1%
0.06 3
 
< 0.1%
0.07 3
 
< 0.1%
0.08 26
 
< 0.1%
0.09 3
 
< 0.1%
0.1 514
0.3%
ValueCountFrequency (%)
199.9 1
< 0.1%
199.13 1
< 0.1%
198.6 1
< 0.1%
198.32 1
< 0.1%
197.75 1
< 0.1%
197.5 1
< 0.1%
197.03 1
< 0.1%
196.35 1
< 0.1%
195.27 1
< 0.1%
195.08 1
< 0.1%

O3
Real number (ℝ)

Distinct12614
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.411483
Minimum0.01
Maximum199.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2024-06-25T19:12:34.689760image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile3.29
Q112.2
median24.3
Q343.65
95-th percentile91.3
Maximum199.92
Range199.91
Interquartile range (IQR)31.45

Descriptive statistics

Standard deviation28.358028
Coefficient of variation (CV)0.87493767
Kurtosis3.1510641
Mean32.411483
Median Absolute Deviation (MAD)14.4
Skewness1.6252776
Sum6601992.3
Variance804.17774
MonotonicityNot monotonic
2024-06-25T19:12:34.855547image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 321
 
0.2%
3 295
 
0.1%
43.02 287
 
0.1%
43.77 265
 
0.1%
22.14 252
 
0.1%
16.48 220
 
0.1%
9.8 169
 
0.1%
4.3 167
 
0.1%
19.6 154
 
0.1%
4.9 152
 
0.1%
Other values (12604) 201411
98.9%
ValueCountFrequency (%)
0.01 19
 
< 0.1%
0.02 10
 
< 0.1%
0.03 21
 
< 0.1%
0.04 16
 
< 0.1%
0.05 10
 
< 0.1%
0.06 8
 
< 0.1%
0.07 10
 
< 0.1%
0.08 8
 
< 0.1%
0.09 6
 
< 0.1%
0.1 110
0.1%
ValueCountFrequency (%)
199.92 1
< 0.1%
199.9 1
< 0.1%
199.3 1
< 0.1%
198.92 1
< 0.1%
198.8 1
< 0.1%
198.45 1
< 0.1%
197.47 1
< 0.1%
197.11 1
< 0.1%
196.6 1
< 0.1%
196.5 1
< 0.1%

Benzene
Real number (ℝ)

ZEROS 

Distinct6295
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5338858
Minimum0
Maximum282.35
Zeros28449
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2024-06-25T19:12:35.006250image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.2
median1.3
Q33.79
95-th percentile13.38
Maximum282.35
Range282.35
Interquartile range (IQR)3.59

Descriptive statistics

Standard deviation14.570066
Coefficient of variation (CV)3.2135936
Kurtosis68.423566
Mean4.5338858
Median Absolute Deviation (MAD)1.25
Skewness7.7301679
Sum923520.8
Variance212.28683
MonotonicityNot monotonic
2024-06-25T19:12:35.151424image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 28449
 
14.0%
0.1 11616
 
5.7%
0.2 3965
 
1.9%
0.3 3660
 
1.8%
0.4 2408
 
1.2%
1.3 2168
 
1.1%
0.15 1920
 
0.9%
0.05 1716
 
0.8%
0.03 1536
 
0.8%
0.6 1466
 
0.7%
Other values (6285) 144789
71.1%
ValueCountFrequency (%)
0 28449
14.0%
0.01 48
 
< 0.1%
0.02 20
 
< 0.1%
0.03 1536
 
0.8%
0.04 18
 
< 0.1%
0.05 1716
 
0.8%
0.06 30
 
< 0.1%
0.07 303
 
0.1%
0.08 1444
 
0.7%
0.09 30
 
< 0.1%
ValueCountFrequency (%)
282.35 1
< 0.1%
279.66 1
< 0.1%
279.19 1
< 0.1%
252.97 1
< 0.1%
245.15 1
< 0.1%
243.4 1
< 0.1%
237.01 1
< 0.1%
236.22 1
< 0.1%
233.53 1
< 0.1%
231.36 1
< 0.1%

Toluene
Real number (ℝ)

ZEROS 

Distinct10805
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.30378
Minimum0
Maximum499.05
Zeros10273
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2024-06-25T19:12:35.299598image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.11
median3.83
Q311.88
95-th percentile47.05
Maximum499.05
Range499.05
Interquartile range (IQR)10.77

Descriptive statistics

Standard deviation28.060733
Coefficient of variation (CV)2.2806595
Kurtosis54.020031
Mean12.30378
Median Absolute Deviation (MAD)3.45
Skewness6.2189103
Sum2506193.9
Variance787.40472
MonotonicityNot monotonic
2024-06-25T19:12:35.456970image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10273
 
5.0%
0.2 2557
 
1.3%
1.1 2268
 
1.1%
0.1 1939
 
1.0%
0.3 1809
 
0.9%
0.5 1055
 
0.5%
0.4 961
 
0.5%
0.6 928
 
0.5%
0.8 904
 
0.4%
0.23 844
 
0.4%
Other values (10795) 180155
88.4%
ValueCountFrequency (%)
0 10273
5.0%
0.01 36
 
< 0.1%
0.02 49
 
< 0.1%
0.03 291
 
0.1%
0.04 51
 
< 0.1%
0.05 321
 
0.2%
0.06 20
 
< 0.1%
0.07 129
 
0.1%
0.08 341
 
0.2%
0.09 97
 
< 0.1%
ValueCountFrequency (%)
499.05 1
< 0.1%
498.9 1
< 0.1%
495.1 1
< 0.1%
492.1 1
< 0.1%
490.43 1
< 0.1%
487.05 1
< 0.1%
485.7 1
< 0.1%
483.55 1
< 0.1%
480.47 1
< 0.1%
480.27 1
< 0.1%

Xylene
Real number (ℝ)

ZEROS 

Distinct4186
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8439361
Minimum0
Maximum423.48
Zeros53724
Zeros (%)26.4%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2024-06-25T19:12:35.605007image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.62
Q32.3
95-th percentile11.07
Maximum423.48
Range423.48
Interquartile range (IQR)2.3

Descriptive statistics

Standard deviation10.035443
Coefficient of variation (CV)3.5287159
Kurtosis349.7413
Mean2.8439361
Median Absolute Deviation (MAD)0.62
Skewness15.41912
Sum579289.88
Variance100.71011
MonotonicityNot monotonic
2024-06-25T19:12:35.761259image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 53724
26.4%
0.1 8861
 
4.4%
2 5008
 
2.5%
0.2 2674
 
1.3%
0.3 2296
 
1.1%
0.4 2099
 
1.0%
0.03 2065
 
1.0%
0.05 2010
 
1.0%
0.15 1879
 
0.9%
0.08 1763
 
0.9%
Other values (4176) 121314
59.6%
ValueCountFrequency (%)
0 53724
26.4%
0.01 60
 
< 0.1%
0.02 26
 
< 0.1%
0.03 2065
 
1.0%
0.04 56
 
< 0.1%
0.05 2010
 
1.0%
0.06 82
 
< 0.1%
0.07 416
 
0.2%
0.08 1763
 
0.9%
0.09 97
 
< 0.1%
ValueCountFrequency (%)
423.48 1
< 0.1%
419.88 1
< 0.1%
379.4 1
< 0.1%
372.45 1
< 0.1%
367.87 1
< 0.1%
360.9 1
< 0.1%
360.05 1
< 0.1%
357.92 1
< 0.1%
353.67 1
< 0.1%
351.35 1
< 0.1%

AQI
Real number (ℝ)

Distinct587
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.31104
Minimum13
Maximum818
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2024-06-25T19:12:35.901889image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile39
Q172
median105
Q3139
95-th percentile303
Maximum818
Range805
Interquartile range (IQR)67

Descriptive statistics

Standard deviation75.328235
Coefficient of variation (CV)0.6261124
Kurtosis4.5744703
Mean120.31104
Median Absolute Deviation (MAD)33
Skewness1.8989153
Sum24506517
Variance5674.343
MonotonicityNot monotonic
2024-06-25T19:12:36.058131image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101 2239
 
1.1%
102 2165
 
1.1%
106 2156
 
1.1%
104 2069
 
1.0%
105 2061
 
1.0%
103 2002
 
1.0%
100 1928
 
0.9%
110 1918
 
0.9%
108 1868
 
0.9%
107 1860
 
0.9%
Other values (577) 183427
90.1%
ValueCountFrequency (%)
13 1
 
< 0.1%
14 13
 
< 0.1%
15 7
 
< 0.1%
16 34
 
< 0.1%
17 67
 
< 0.1%
18 75
 
< 0.1%
19 101
 
< 0.1%
20 121
0.1%
21 108
0.1%
22 262
0.1%
ValueCountFrequency (%)
818 1
 
< 0.1%
805 1
 
< 0.1%
771 1
 
< 0.1%
755 1
 
< 0.1%
749 1
 
< 0.1%
734 3
< 0.1%
722 1
 
< 0.1%
706 1
 
< 0.1%
705 1
 
< 0.1%
700 2
< 0.1%

AQI_Bucket
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
Moderate
88654 
Satisfactory
69531 
Good
22852 
Poor
11704 
Very Poor
9147 

Length

Max length12
Median length9
Mean length8.714001
Min length4

Characters and Unicode

Total characters1774981
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowModerate
2nd rowModerate
3rd rowModerate
4th rowModerate
5th rowModerate

Common Values

ValueCountFrequency (%)
Moderate 88654
43.5%
Satisfactory 69531
34.1%
Good 22852
 
11.2%
Poor 11704
 
5.7%
Very Poor 9147
 
4.5%
Severe 1805
 
0.9%

Length

2024-06-25T19:12:36.212743image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-25T19:12:36.345524image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
moderate 88654
41.7%
satisfactory 69531
32.7%
good 22852
 
10.7%
poor 20851
 
9.8%
very 9147
 
4.3%
severe 1805
 
0.8%

Most occurring characters

ValueCountFrequency (%)
o 245591
13.8%
a 227716
12.8%
t 227716
12.8%
e 191870
10.8%
r 189988
10.7%
d 111506
6.3%
M 88654
 
5.0%
y 78678
 
4.4%
S 71336
 
4.0%
c 69531
 
3.9%
Other values (8) 272395
15.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1552994
87.5%
Uppercase Letter 212840
 
12.0%
Space Separator 9147
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 245591
15.8%
a 227716
14.7%
t 227716
14.7%
e 191870
12.4%
r 189988
12.2%
d 111506
7.2%
y 78678
 
5.1%
c 69531
 
4.5%
s 69531
 
4.5%
f 69531
 
4.5%
Other values (2) 71336
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
M 88654
41.7%
S 71336
33.5%
G 22852
 
10.7%
P 20851
 
9.8%
V 9147
 
4.3%
Space Separator
ValueCountFrequency (%)
9147
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1765834
99.5%
Common 9147
 
0.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 245591
13.9%
a 227716
12.9%
t 227716
12.9%
e 191870
10.9%
r 189988
10.8%
d 111506
6.3%
M 88654
 
5.0%
y 78678
 
4.5%
S 71336
 
4.0%
c 69531
 
3.9%
Other values (7) 263248
14.9%
Common
ValueCountFrequency (%)
9147
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1774981
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 245591
13.8%
a 227716
12.8%
t 227716
12.8%
e 191870
10.8%
r 189988
10.7%
d 111506
6.3%
M 88654
 
5.0%
y 78678
 
4.4%
S 71336
 
4.0%
c 69531
 
3.9%
Other values (8) 272395
15.3%

Interactions

2024-06-25T19:12:29.787486image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:11.737914image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:13.270103image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:14.775408image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:16.255673image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:17.712448image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:19.177681image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:20.777632image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:22.276093image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:23.825743image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:25.349234image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:26.789063image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:28.343435image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:29.905456image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:11.862916image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:13.386425image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:14.895957image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:16.366535image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:17.821827image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:19.303211image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:20.913505image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:22.401061image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:23.935119image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:25.457648image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:26.898410image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:28.452833image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:30.024656image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:11.972319image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:13.502355image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:15.008665image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:16.462841image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:17.931227image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:19.418150image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:21.028652image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:22.519354image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:24.060119image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:25.567049image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:27.007783image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:28.562217image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:30.136992image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:12.081695image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:13.602592image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:15.108820image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:16.580154image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:18.024981image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:19.527526image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:21.148615image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:22.641442image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:24.177208image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:25.676424image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:27.117188image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:28.671592image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:30.255793image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:12.197542image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:13.718782image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:15.233042image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:16.702742image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:18.145683image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:19.636930image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:21.256749image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:22.749204image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:24.282527image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:25.770177image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:27.355107image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:28.765342image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:30.365175image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:12.300803image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:13.837418image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:15.333878image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:16.813925image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:18.247407image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:19.883304image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:21.372495image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:22.858616image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:24.391905image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:25.879547image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:27.465811image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:28.874713image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:30.490547image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:12.425804image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:13.970656image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:15.446777image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:16.918554image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:18.353532image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:19.977087image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:21.480140image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:22.967960image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:24.501286image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:25.988931image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:27.559608image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:28.968475image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:30.615576image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:12.535207image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:14.083337image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:15.560309image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:17.030314image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:18.484592image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:20.086460image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:21.592162image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:23.092959image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:24.626248image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:26.098300image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:27.668972image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:29.077809image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:30.740577image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:12.660208image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:14.204785image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:15.680653image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:17.143976image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:18.596475image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:20.211432image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:21.706316image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:23.223403image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:24.751273image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:26.210901image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:27.793941image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:29.213848image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:30.865570image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:12.785206image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:14.321922image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:15.793762image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:17.253342image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:18.736349image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:20.336461image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:21.834536image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:23.357587image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:24.876282image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:26.335942image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:27.918966image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:29.335806image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:30.997220image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:12.910177image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:14.438633image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:15.910405image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:17.381622image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:18.833973image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:20.449296image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:21.945572image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:23.466377image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:24.985660image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:26.445287image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:28.028350image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:29.446623image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:31.106053image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:13.028454image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:14.555327image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:16.024154image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:17.493727image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:18.958932image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:20.554737image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:22.048011image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:23.591368image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:25.095040image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:26.554691image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:28.122103image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:29.558736image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:31.232549image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:13.145092image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:14.666031image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:16.131709image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:17.587476image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:19.068305image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:20.648519image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:22.163263image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:23.700744image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:25.224191image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:26.664067image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:28.234058image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T19:12:29.664025image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Missing values

2024-06-25T19:12:31.388646image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-25T19:12:31.698739image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PM2.5PM10NONO2NOxNH3COSO2O3BenzeneTolueneXyleneAQIAQI_Bucket
16104.00148.501.9323.0013.759.800.115.30117.620.3010.400.23155.0Moderate
1794.50142.001.3316.259.759.650.117.00136.230.287.100.15159.0Moderate
1882.75126.501.4714.839.079.700.115.40149.920.204.550.08173.0Moderate
2168.50117.001.3513.608.357.400.121.80161.700.102.300.00191.0Moderate
2269.25112.251.5211.807.559.250.121.38161.680.102.350.00191.0Moderate
2370.00107.002.8030.3318.406.150.118.90147.970.103.700.00191.0Moderate
2472.75120.251.5026.7215.4510.780.116.03137.200.105.700.03191.0Moderate
2581.50134.751.1018.7810.8814.730.112.93146.030.207.150.10191.0Moderate
2685.00142.501.6226.2015.2714.500.212.90123.000.207.150.00191.0Moderate
2791.50145.750.9818.8810.8314.120.215.22146.520.208.100.05191.0Moderate
PM2.5PM10NONO2NOxNH3COSO2O3BenzeneTolueneXyleneAQIAQI_Bucket
2543899110.35184.3294.3357.20151.5537.671.625.074.2982.8577.282.99230.0Poor
2543900106.72172.5394.2559.67153.9336.921.635.754.4476.1769.492.82231.0Poor
2543901108.28173.97128.1852.75180.9334.051.696.224.5671.1665.782.66230.0Poor
2543902113.67208.03141.6060.10201.7335.281.757.034.5773.4170.402.66228.0Poor
2543903122.75223.7795.5069.03164.5038.381.478.076.6269.9867.202.30226.0Poor
2543904102.80192.3844.9569.05114.0340.231.067.5316.7065.4259.691.84223.0Poor
254390585.50161.3516.1565.2281.3239.080.798.6020.4757.1451.311.41222.0Poor
254390673.75143.658.4052.6561.0838.530.669.8226.5853.7151.381.25219.0Poor
254390771.50133.385.6045.0350.6242.620.559.5728.2856.8056.271.21217.0Poor
254390854.47117.124.2039.0043.1748.020.629.2031.6356.0455.581.12215.0Poor

Duplicate rows

Most frequently occurring

PM2.5PM10NONO2NOxNH3COSO2O3BenzeneTolueneXyleneAQIAQI_Bucket# duplicates
058.14112.6126.4435.3161.7548.571.355.665.710.01.242.17108.0Moderate10
458.14112.6126.4435.3161.7548.571.355.665.710.01.242.17123.0Moderate4
258.14112.6126.4435.3161.7548.571.355.665.710.01.242.17116.0Moderate3
158.14112.6126.4435.3161.7548.571.355.665.710.01.242.17109.0Moderate2
358.14112.6126.4435.3161.7548.571.355.665.710.01.242.17117.0Moderate2